scholarly journals A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

2021 ◽  
Vol 7 ◽  
pp. e632
Author(s):  
Ward Ahmed Al-Hussein ◽  
Miss Laiha Mat Kiah ◽  
Por Lip Yee ◽  
B B. Zaidan
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Research Area ◽  
Transportation Systems ◽  
Driver Behaviour ◽  
Future Directions ◽  
Research Directions ◽  
Assistance Systems ◽  
The Common ◽  
Near Future

In the plan and development of Intelligent Transportation Systems (ITS), understanding drivers behaviour is considered highly valuable. Reckless driving, incompetent preventive measures, and the reliance on slow and incompetent assistance systems are attributed to the increasing rates of traffic accidents. This survey aims to review and scrutinize the literature related to sensor-based driver behaviour domain and to answer questions that are not covered so far by existing reviews. It covers the factors that are required in improving the understanding of various appropriate characteristics of this domain and outlines the common incentives, open confrontations, and imminent commendations from former researchers. Systematic scanning of the literature, from January 2014 to December 2020, mainly from four main databases, namely, IEEEXplore, ScienceDirect, Scopus and Web of Science to locate highly credible peer-reviewed articles. Amongst the 5,962 articles found, a total of 83 articles are selected based on the author’s predefined inclusion and exclusion criteria. Then, a taxonomy of existing literature is presented to recognize the various aspects of this relevant research area. Common issues, motivations, and recommendations of previous studies are identified and discussed. Moreover, substantial analysis is performed to identify gaps and weaknesses in current literature and guide future researchers into planning their experiments appropriately. Finally, future directions are provided for researchers interested in driver profiling and recognition. This survey is expected to aid in emphasizing existing research prospects and create further research directions in the near future.

Modified fuzzy-based greedy routing protocol for VANETs

2020 ◽  
Vol 39 (6) ◽  
pp. 8357-8364
Author(s):  
Thompson Stephan ◽  
Ananthnarayan Rajappa ◽  
K.S. Sendhil Kumar ◽  
Shivang Gupta ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocol ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Research Area ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Greedy Routing ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

Fog-based Intelligent Transportation System for Traffic Light Optimization

2021 ◽  
Vol 58 (S) ◽  
pp. 29-35
Author(s):  
Muhammad Rusyadi Ramli ◽  
Riesa Krisna Astuti Sakir ◽  
Dong-Seong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Light ◽  
Traffic Condition ◽  
Dilemma Zone ◽  
Traffic Lights

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

scholarly journals 2 + 1 Highways: Overview and Future Directions

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Manuel Romana ◽  
Marilo Martin-Gasulla ◽  
Ana T. Moreno
Keyword(s):  
Urban Areas ◽  
High Performance ◽  
White Paper ◽  
Future Directions ◽  
Research Directions ◽  
Traffic Volumes ◽  
Primary Means ◽  
The Common ◽  
Rural Access ◽  
Transportation Research

Most of the rural transportation system is composed of two-lane highways, and many of them serve as the primary means for rural access to urban areas and freeways. In some highways, traffic volumes can be not high enough to justify a four-lane highway but higher than can be served by isolated passing lanes, or can present high number of head-on collisions. In those conditions, 2 + 1 highways are potentially applicable. This type of highway is used to provide high-performance highways as intermediate solution between the common two-lane highway and the freeway. Successful experiences reported in Germany, Sweden, Finland, Poland, or Texas (US) may suggest that they are potentially applicable in other countries. The objective of this white paper is to provide an overview of the past practice in 2 + 1 highways and discuss the research directions and challenges in this field, specially focusing on, but not limited to, operational research in association with the activities of the Subcommittee on Two-Lane Highways (AHB40 2.2) of the Transportation Research Board. The significance of this paper is twofold: (1) it provides wider coverage of past 2 + 1 highways design and evaluation, and (2) it discusses future directions of this field.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

A Survey of Approaches for Estimating Meteorological Visibility Distance Under Foggy Weather Conditions

2020 ◽  
pp. 65-92 ◽  
Author(s):  
Faouzi Kamoun ◽  
Hazar Chaabani ◽  
Fatma Outay ◽  
Ansar-Ul-Haque Yasar
Keyword(s):  
Intelligent Transportation Systems ◽  
Situational Awareness ◽  
Wide Spectrum ◽  
Distance Estimation ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Driver Assistance Systems ◽  
Research Issues ◽  
Visibility Distance ◽  
Assistance Systems

The immaturity of fog abatement technologies for highway usage has led to growing interest towards developing intelligent transportation systems that are capable of estimating meteorological visibility distance under foggy weather conditions. This capability is crucial to support next-generation cooperative situational awareness and collision avoidance systems as well as onboard driver assistance systems. This chapter presents a survey and a comprehensive taxonomy of daytime visibility distance estimation approaches based on a review and synthesis of the literature. The proposed taxonomy is both comprehensive (i.e., captures a wide spectrum of earlier contributions) and effective (i.e., enables easy comparison among previously proposed approaches). The authors also highlight some open research issues that warrant further investigation.

scholarly journals When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

2021 ◽  
Vol 11 (20) ◽  
pp. 9680
Author(s):  
Xuan Zhou ◽  
Ruimin Ke ◽  
Hao Yang ◽  
Chenxi Liu
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Research Community ◽  
Edge Computing ◽  
Data Driven ◽  
Future Directions ◽  
Critical Part ◽  
Modern Cities ◽  
Computing Vision

The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), such as a main building block of modern cities and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.

Proactive Decision Making for ITS Communication

2020 ◽  
pp. 197-226 ◽  
Author(s):  
Rodrigo Silva ◽  
Christophe Couturier ◽  
Thierry Ernst ◽  
Jean-Marie Bonnin
Keyword(s):  
Decision Making ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Network Resources ◽  
Communication Architecture ◽  
Cooperative Vehicles ◽  
Near Future ◽  
Cooperative Intelligent Transportation Systems ◽  
Exchange Data

Demand from different actors for extended connectivity where vehicles can exchange data with other vehicles, roadside infrastructure, and traffic control centers have pushed vehicle manufacturers to invest in embedded solutions, which paves the way towards cooperative intelligent transportation systems (C-ITS). Cooperative vehicles enable the development of an ecosystem of services around them. Due to the heterogeneousness of such services and their specific requirements, as well as the need for network resources optimization for ubiquitous connectivity, it is necessary to combine existing wireless technologies, providing applications with a communication architecture that hides such underlying access technologies specificities. Due to vehicles' high velocity, their connectivity context can change frequently. In such scenario, it is necessary to take into account the short-term prevision about network environment; enabling vehicles proactively manage their communications. This chapter discusses about the use of near future information to proactive decision-making process.

A novel message authentication method for VANET without RSU

2016 ◽  
Vol 33 (8) ◽  
pp. 2288-2301 ◽  
Author(s):  
Alan Dahgwo Yein ◽  
Chih-Hsueh Lin ◽  
Yu-Hsiu Huang ◽  
Wen-Shyong Hsieh ◽  
Chung-Nan Lee ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Ad Hoc ◽  
Transportation Systems ◽  
Security Requirements ◽  
Message Authentication ◽  
Tier 2 ◽  
Content Type ◽  
Secret Keys ◽  
Trust Authority

Purpose Riding on the wave of intelligent transportation systems, the vehicular ad hoc network (VANET) is becoming a popular research topic. VANET is designed to build an environment where the vehicles can exchange information about the traffic conditions or vehicle situation to help the vehicles avoid traffic accidents or traffic jams. In order to keep the privacy of vehicles, the vehicles must be anonymous and the routing must be untraceable while still being able to be verified as legal entities. The paper aims to discuss these issues. Design/methodology/approach The exchanged messages must be authenticated to be genuine and verified that they were sent by a legal vehicle. The vehicles also can mutually trust and communicate confidentially. In VANETs, road-side units (RSUs) are installed to help the vehicles to obtain message authentication or communicate confidentially. However, the coverage of RSUs is limited due to the high cost of wide area installation. Therefore the vehicles must be able to obtain message authentication by themselves – without an RSU. Findings The authors take the concept of random key pre-distribution used in wireless sensor networks, modify it into a random secret pre-distribution, and integrate it with identity-based cryptography to make anonymous message authentication and private communication easier and safer. The authors construct a two-tier structure. The tier 1, trust authority, assigns n anonymous identities and embeds n secrets into these identities to be the private secret keys for the tier 2, registered vehicles. At any time, the vehicles can randomly choose one of n anonymous identities to obtain message authentication or communicate confidentially with other vehicles. Originality/value The processes of building neighbor set, setting pairing value, and message authenticating are proposed in this paper. The proposed method can protect against the attacks of compromising, masquerading, forging, and replying, and can also achieve the security requirements of VANET in message authentication, confidential communication, anonymity, and un-traceability. The performance of the proposed method is superior to the related works.

Role of wireless body area networks in intelligent transportation systems: A survey

2021 ◽  
Vol 11 ◽  
Author(s):  
Rishu Chhabra ◽  
S. Beski Prabaharan ◽  
Rashmi Aggarwal
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Research Area ◽  
Transportation Systems ◽  
The Body ◽  
Sensor Technology ◽  
Smart Devices ◽  
Future Research ◽  
Body Sensors

: The employment of wireless body sensors for different applications like healthcare and transportation systems has been a key research area. The hybrid systems that combine the wearable wireless sensor technology for safety applications in transportation infrastructure to save the life of the drivers and Vulnerable Road Users (VRU) like cyclists and pedestrians is another step towards the aim of Intelligent Transportation Systems (ITS) to improve safety on roads. The systems in the literature involve the use of sensors fixed on or around the body of the driver and/or pedestrian, sensors embedded in smart devices like smartphone, smartwatch, and tablets etc. to alert the drivers and pedestrians in advance to prevent the probability of collision and prevent road accidents. The sensors are able to detect the behavior of the driver and identify whether he/she is fit for driving a vehicle. The presence of a distracted pedestrian is another cause of collision and the nearby vehicles should be informed about the same. In this paper, different systems in the literature that employ wireless body sensors to improve safety of the commute have been reviewed. The techniques reviewed in this paper focus on safety of the driver, VRU/pedestrian and both. The paper concludes by presenting key challenges and future research directions to prevent accidents by the integration of wireless body sensor network as a part of intelligent transportation system.

Design Guidelines on Deep Learning–based Pedestrian Detection Methods for Supporting Autonomous Vehicles

2021 ◽  
Vol 54 (6) ◽  
pp. 1-36
Author(s):  
Azzedine Boukerche ◽  
Mingzhi Sha
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Traffic Accidents ◽  
Pedestrian Detection ◽  
Transportation Systems ◽  
Design Guidelines ◽  
Detection Methods ◽  
Multi Scale ◽  
Present Design

Intelligent transportation systems (ITS) enable transportation participants to communicate with each other by sending and receiving messages, so that they can be aware of their surroundings and facilitate efficient transportation through better decision making. As an important part of ITS, autonomous vehicles can bring massive benefits by reducing traffic accidents. Correspondingly, much effort has been paid to the task of pedestrian detection, which is a fundamental task for supporting autonomous vehicles. With the progress of computational power in recent years, adopting deep learning–based methods has become a trend for improving the performance of pedestrian detection. In this article, we present design guidelines on deep learning–based pedestrian detection methods for supporting autonomous vehicles. First, we will introduce classic backbone models and frameworks, and we will analyze the inherent attributes of pedestrian detection. Then, we will illustrate and analyze representative pedestrian detectors from occlusion handling, multi-scale feature extraction, multi-perspective data utilization, and hard negatives handling these four aspects. Last, we will discuss the developments and trends in this area, followed by some open challenges.

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